KevinZai $ccc-data — Data domain hub
Load ONE skill. Get the entire data domain. 8 skills in one.
Sub-Skills
| # | Skill | Focus |
|---|---|---|
| 1 | data-pipeline | ETL/ELT pipelines — Airflow, dbt, Dagster, incremental loads |
| 2 | sql-optimization | SQL optimization — query analysis, indexes, execution plans |
| 3 | data-visualization | Charts and dashboards — D3, Chart.js, Tremor, Recharts |
| 4 | machine-learning | ML model development — scikit-learn, PyTorch, TensorFlow |
| 5 | data-quality | Data validation, schema enforcement, quality monitoring |
| 6 | analytics-setup | Analytics implementation — PostHog, Mixpanel, GA4 |
| 7 | reporting | Automated report generation and scheduling |
| 8 | vector-search | Vector database — Pinecone, pgvector, Qdrant with semantic search |
No expressed intent? Present the top 3 sub-skills + "More…" as an AskUserQuestion picker (≤4 options).
Routing Matrix
| Your Intent | Route To |
|---|---|
| "Data pipeline" / "ETL" / "ELT" | data-pipeline |
| "Slow queries" / "SQL optimization" | sql-optimization |
| "Charts" / "Dashboard" / "Visualization" | data-visualization |
| "ML model" / "Train a model" | machine-learning |
| "Data quality" / "Validation" | data-quality |
| "Analytics" / "Tracking events" | analytics-setup |
| "Automated reports" | reporting |
| "Semantic search" / "Vector search" / "Embeddings" | vector-search |
Files API Integration
For large datasets and data files, the Files API can ingest CSVs, JSON, Parquet, and other formats directly — avoiding token limits for bulk data analysis. Use data-ingestion from ccc-research for document-scale inputs.
Campaign Templates
Analytics Stack Setup
analytics-setup→ PostHog/Mixpanel/GA4 event trackingdata-pipeline→ sync analytics data to warehousedata-visualization→ build dashboards from warehouse datareporting→ automate periodic reports
ML Feature Build
data-quality→ validate and clean training datasql-optimization→ optimize feature extraction queriesmachine-learning→ model development + evaluationdata-visualization→ model performance chartsvector-search→ if feature requires semantic similarity
When to invoke this skill
Example 1
- user: design an ETL pipeline to sync our Postgres data to a data warehouse
- assistant: Loads ccc-data and routes to data-pipeline for Airflow/dbt/Dagster pipeline design with incremental loads.
Example 2
- user: my SQL queries are slow — optimize them
- assistant: Loads ccc-data and routes to sql-optimization for query analysis, index recommendations, and execution plan review.
Example 3
- user: add semantic search to our product catalog
- assistant: Loads ccc-data and routes to vector-search for pgvector/Pinecone/Qdrant setup with embedding pipeline.
⚙️ Fable contract: plan before build · verifier ≠ worker · prove before alarm · loops need gates · leave durable state —
rules/fable-method.md
(On Codex, present these options as a numbered list and ask the user to reply with a number — AskUserQuestion is Claude-only.)